Node Details
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Name:
reactAgentChat -
Type:
AgentExecutor - Category: Agents
Description
This agent implements the ReAct (Reason + Act) framework, which allows it to alternate between reasoning about a problem and taking actions to solve it. It’s designed to work seamlessly with chat models, making it particularly effective for interactive, multi-turn conversations that involve complex problem-solving.Parameters
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Allowed Tools (Required)
- Type: Tool[]
- Description: A list of tools that the agent can use to perform tasks or gather information.
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Chat Model (Required)
- Type: BaseChatModel
- Description: The chat model used for generating responses and making decisions.
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Memory (Required)
- Type: BaseChatMemory
- Description: The memory component used to store and retrieve conversation history.
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Input Moderation (Optional)
- Type: Moderation[]
- Description: Moderation tools to detect and prevent harmful input.
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Max Iterations (Optional)
- Type: number
- Description: The maximum number of reasoning-action cycles the agent will perform for a single input.
- Additional Params: true
Input
- A string containing the user’s message or query.
Output
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A string or object containing the agent’s response, which may include:
- The final answer or solution to the user’s query
- Intermediate steps of reasoning and actions taken
- Any relevant information gathered during the process
How It Works
- The agent is initialized with the provided tools, chat model, memory, and optional parameters.
- It receives a user input, which is first checked by any specified moderation tools.
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The agent then enters a loop of reasoning and acting:
- It analyzes the current state and the task at hand
- Decides on the next action (which may involve using a tool)
- Executes the action and observes the result
- Updates its understanding based on the action’s outcome
- This loop continues until the agent reaches a conclusion or hits the maximum number of iterations.
- The final response is generated, incorporating the reasoning process and results.
- The conversation history is updated in the memory for future context.
Use Cases
- Complex problem-solving in a conversational context
- Multi-step task execution with explanations
- Interactive tutoring or educational systems
- Research assistants that can reason and gather information
- Customer support systems for complex inquiries
- Analytical tools that require both data processing and natural language interaction
Special Features
- ReAct Framework: Implements the Reason + Act loop for sophisticated problem-solving.
- Chat Model Optimization: Specifically designed to work well with chat models for natural conversations.
- Tool Integration: Can use a variety of tools to enhance its capabilities and perform actions.
- Memory Management: Maintains conversation history for contextual understanding.
- Moderation: Can implement input moderation to ensure safe interactions.
- Vision Support: If the chat model supports vision capabilities, the agent can process and respond to image inputs.
- Streaming: Supports streaming responses for real-time interaction.
Notes
- The agent uses a sophisticated prompt structure that encourages step-by-step reasoning and action.
- It can handle multi-modal inputs if the underlying chat model supports it (e.g., text and images).
- The max iterations parameter can be used to control the depth of the agent’s problem-solving attempts.
- This agent is particularly effective for tasks that require a combination of analytical thinking and information gathering or action execution.